Method and apparatus for generating an electrode stimulation signal in a neural auditory prosthesis

ABSTRACT

An auditory stimulation signal processing has a plurality of signal inputs adapted to receive a plurality of frequency bin signals, a selection probability value assigner, a random selector, and an electrode stimulation signal generator. The selection probability assigner is adapted to assign a selection probability value to at least one frequency bin signal of the plurality of frequency bin signals. The random selector is adapted to select one frequency bin signal from the plurality of frequency bin signals by means of a random process taking into account the selection probability value assigned to the at least on frequency bin signal. The electrode stimulation generator is adapted to generate an electrode stimulation signal for application to an electrode of a neural auditory prosthesis, the electrode corresponding to a frequency of the selected frequency bin signal. A corresponding method and a computer readable digital storage medium are also disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of copending InternationalApplication No. PCT/EP2010/069522, filed Dec. 13, 2010, which isincorporated herein by reference in its entirety, and additionallyclaims priority from U.S. Application No. 61/310,425, filed Mar. 4,2010, which is incorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

Embodiments of the invention relate to a method and an apparatus forgenerating an electrode stimulation signal in a neural auditoryprosthesis. In embodiments of the invention, a selection of a particularelectrode from a plurality of available electrodes is partially random.

The field of the present invention relates to an auditory prosthesis,like a cochlear implant or a brainstem implant, configured for deliveryof non-simultaneous stimulation through at least two active electrodes.

The global deaf population is roughly estimated to be 0.1% of the totalpopulation. There are various causes of deafness including infectious,traumatic, toxic, age-related, occupational, and genetic disorders. Inthe majority of the cases the inner ear, i.e. the cochlear structure, isdamaged.

Nowadays, however, there are ways to bypass the peripheral auditorysystem and directly stimulate auditory nerve fibers. This process ismade available by the cochlear implants (CIs), which have been thetarget of intensive research for over fifty years by now, and by themore recent brainstem implants (BIs). Even though cochlear implants arethe most successful neural prosthesis ever, hearing can only bepartially restored by them. Patients achieve an average of almost 80% inspeech recognition tests under quiet conditions (without lip-reading)until the end of the second year after implantation (cf. the article“Evidence that cochlear-implanted deaf patients are better multisensoryintegrators” by Rouger et al., published in Proc. Nat. Acad. of Sciences(PMAS), vol. 104 (17), pp. 7295-7300, 2007 and in Journal of Acoust.Soc. Am., vol. 111 (5), Pt. 1, May 2002), but most cochlear implantrecipients remain unable to enjoy music or to distinguish among complexsounds. Moreover, speech recognition in noisy environments is still achallenge for most cochlear implant recipients.

Even today a number of modern CI systems employ speech processingstrategies that are still based on very “simple” filter-banks (e.g. theFast Fourier Transform (FFT), dating back to the mid-1960s) to mimic thecomplex functionality of the human auditory system. On the other hand,numerous biologically motivated models of the basilar membrane (BM,organ of the cochlear filtering) and of auditory structures—havingstrong non-linear properties beyond the BM have been developed duringthe last 20 years.

Recent research of the inventor indicates that time has come forpractical CI/BI systems and theoretical ear models to converge andfacilitate a higher quality of restoration of hearing.

U.S. Patent Application Publication No. 2009/0030486 A1 discloses amethod of generating a control signal for a cochlear implant based on anaudio signal. An activity pattern over time at a plurality of inner earcells of an auditory model is calculated. Activity events within theactivity pattern are filtered out based on a recognition of acharacteristic pattern in the activity pattern, whereby clearedinformation is obtained. The cleared information is further used as acontrol signal for the cochlear implant, or the control signal for thecochlear implant is derived from the cleared information. The ideadisclosed in the '486 U.S. patent application is based on the knowledgethat in an activity pattern, a multitude of activity impulses is presentat a plurality of inner ear cells of an auditory model over a time,which are not relevant for a patient's auditory sensation. Thus, acharacteristic pattern in the nerve activity pattern can be recognizedand, based on the recognition of the characteristic pattern, some of theactivity events can be filtered out because they are only of secondaryimportance for a patient's perception. An example of the recognition ofthe characteristic pattern is a Hough-based pattern classification.

SUMMARY

According to an embodiment, a method of generating a control signal fora neural auditory prosthesis, based on an audio signal, may have thesteps of receiving a plurality of frequency bin signals; determiningwhether one electrode of the neural auditory prosthesis had beenselected for stimulation during a previous stimulation cycle;attenuating a corresponding frequency bin signal that corresponds to thedetermined electrode stimulated during the previous stimulation cycle;assigning a selection probability value to at least one frequency binsignal of the plurality of frequency bin signals; selecting onefrequency bin signal of the plurality of frequency bin signals by meansof a random process taking into account the selection probability valueassigned to the at least one frequency bin signal; and generating anelectrode stimulation signal for application to an electrode of theneural auditory prosthesis corresponding to a frequency of the selectedfrequency bin signal.

According to another embodiment, a computer readable digital storagemedium may have stored thereon a computer program having a program codefor performing, when running on a computer, a method for signalprocessing of a signal in a neural auditory prosthesis to generate acontrol signal for the neural auditory prosthesis, wherein the methodmay have the steps of receiving a plurality of frequency bin signals;determining whether one electrode of the neural auditory prosthesis hadbeen selected for stimulation during a previous stimulation cycle;attenuating a corresponding frequency bin signal that corresponds to thedetermined electrode stimulated during the previous stimulation cycle;assigning a selection probability value to at least one frequency binsignal of the plurality of frequency bin signals; selecting onefrequency bin signal of the plurality of frequency bin signals by meansof a random process taking into account the selection probability valueassigned to the at least one frequency bin signal; and generating anelectrode stimulation signal for application to an electrode of theneural auditory prosthesis corresponding to a frequency of the selectedfrequency bin signal.

According to another embodiment, a auditory stimulation signalprocessing device may have a plurality of signal inputs adapted toreceive a plurality of frequency bin signals; an amplitude equalizeradapted to perform an amplitude equalization on the received pluralityof frequency bin signals and to determine whether one electrode of theneural auditory prosthesis had been selected for stimulation during atleast one previous stimulation cycle among a certain number of previousstimulation cycles and to attenuate a corresponding frequency bin signalthat corresponds to the determined electrode stimulated during theprevious stimulation cycle among the certain number of the laststimulation cycles; a selection probability value assigner adapted toassign a selection probability value to at least one frequency binsignal of the plurality of frequency bin signals; a random selectoradapted to select one frequency bin signal from the plurality offrequency bin signals by means of a random process taking into accountthe selection probability value assigned to the at least on frequencybin signal; and an electrode stimulation signal generator adapted togenerate an electrode stimulation signal for application to an electrodeof a neural auditory prosthesis, the electrode corresponding to afrequency of the selected frequency bin signal.

According to an embodiment, a method of generating a control signal fora neural auditory prosthesis, based on an audio signal, may have theactions of: receiving a plurality of frequency bin signals; assigning aselection probability value to at least one frequency bin signal of theplurality of frequency bin signals; selecting one frequency bin signalof the plurality of frequency bin signals by means of a random processtaking into account the selection probability value assigned to the atleast one frequency bin signal; and generating an electrode stimulationsignal for application to an electrode of the neural auditory prosthesiscorresponding to the frequency of the selected frequency bin signal.

According to another embodiment, an auditory stimulation signalprocessing device may have: a plurality of signal inputs adapted toreceive a plurality of frequency bin signals; a selection probabilityvalue assigner adapted to assign a selection probability value to atleast one frequency bin signal of the plurality of frequency binsignals; a random selector adapted to select one frequency bin signalfrom the plurality of frequency bin signals by means of a random processtaking into account the selection probability value assigned to the atleast one frequency bin signal; and an electrode stimulation signalgenerator adapted to generate an electrode stimulation signal forapplication to an electrode of a neural auditory prosthesis, theelectrode corresponding to a frequency of the selected frequency binsignal.

Another embodiment may have a computer program having program code forperforming, when running on a computer, a method of generating a controlsignal for a neural auditory prosthesis as mentioned above.

In embodiments of the invention, an amplitude equalization may beperformed on the received plurality of frequency bin signals. Hence, theauditory stimulation signal processing device may further comprise anamplitude equalizer adapted to perform an amplitude equalization on thereceived plurality of frequency bin signals.

Embodiments of the invention are based on the recognition that a degreeof randomness in the electrode stimulation signal or the control signalrelative thereto is likely to add to a bio-compatibility of theelectrode stimulation signal. A possible explanation is that the neuralauditory prosthesis interfaces with a remaining, healthy part of therecipient's auditory sense which is used to, or geneticallypredetermined, receiving nervous stimuli from a natural, now defunctpart of the auditory sense.

In embodiments of the disclosed teachings, the plurality of frequencybin signals may be received from a filter bank based on a stimulation ofat least one of a basilar membrane and an inner hair cell.

In embodiments of the teachings disclosed herein, a plurality ofselection probability values may be assigned to a plurality of frequencybin signals, respectively. Typically, at least two probability selectionvalues for at least two frequency bin signals are non-zero, that isthese at least two selection probability values have non-negligiblevalues so that a random selection between the at least two frequency binsignals is performed.

In embodiments of the disclosed teachings, frequency bin signals havinga high magnitude with respect to other frequency bin signals may beassigned high-valued selection probability values compared to theselection probability values assigned to frequency bin signals having arelatively lower magnitude. The selection probability assigner of theauditory stimulation signal processing device may be further adapted toassign high-valued selection probability values to frequency bin signalshaving a high magnitude with respect to other ones of the plurality offrequency bin signals.

In embodiments of the disclosed teachings the method may furthercomprise:

-   -   determining whether an electrode of the neural auditory        prosthesis had been selected for stimulation during a previous        stimulation cycle; and    -   attenuating a corresponding frequency bin signal that        corresponds to the determined electrode stimulated during the        previous stimulation cycle.

In the case of an auditory stimulation signal processing device, thisfunctionality may be provided by the amplitude equalizer, i.e., theamplitude equalizer may be adapted to determine whether one electrode ofthe neural auditory prosthesis had been selected for stimulation duringat least one previous stimulation cycle among a certain number ofprevious stimulation cycles and to attenuate a corresponding frequencybin signal that corresponds to the determined electrode stimulatedduring the previous stimulation cycle among the certain number of thelast stimulation cycles. In the alternative to the amplitude equalizer,this functionality or an equivalent functionality may be provided byanother component of the auditory stimulation signal processing device.

In embodiments of the teachings disclosed herein, the method maycomprise further actions prior to assigning the selection probabilityvalues to a plurality of frequency bin signals. One of these actions maybe a mapping of an amplitude of each one of the plurality of frequencybin signals to a loudness-map representation of the amplitude, themapping being based on patient-specific conditions. The auditorystimulation signal processing device may further comprise a loudnessmapping function connected to the signal input and adapted to map anamplitude of at least one of the plurality of frequency bin signals to aloudness-mapped representation of the amplitude, the mapping being basedon patient-specific conditions.

In embodiments of the disclosed teachings, the method may furthercomprise an action of (randomly) varying a stimulation signal generationparameter used for generating the electrode stimulation signal. Thisrelates to other aspects of adding a certain degree of randomness to theelectrode stimulation signal generation.

In embodiments of the disclosed teachings, the parameter modifier maycomprise a randomizer so that the variation of the stimulation signalgeneration parameter is based on a random process.

In embodiments of the disclosed teachings, the stimulation signalgeneration parameter may affect a waveform of the electrode stimulationsignal. In particular, a template for creating the electrode stimulationsignal may comprise a temporal gap in which the template issubstantially zero-valued between two non-zero sections. The stimulationsignal generation parameter subject to random variation may be aduration of the temporal gap between the two non-zero sections.

In the following specification, the neural auditory prosthesis will bedescribed as a cochlear implant. It is, however, clear for a personskilled in the art that it is possible to employ the inventive approachwith other types of neural auditory prostheses.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the teachings disclosed herein will be detailedsubsequently referring to the appended drawings, in which:

FIG. 1 is a schematic block diagram illustrating an overview of a neuralauditory prosthesis;

FIG. 2 shows frequency-over-time representations of some signals withinthe neural auditory prosthesis;

FIG. 3 is a schematic block diagram of components of the neural auditoryprosthesis;

FIGS. 4A and 4B show a schematic flow chart of a method according to theteachings disclosed herein;

FIG. 5 is a graph illustrating a characteristic of a basilar membrane asa function of loudness;

FIG. 6 is a schematic flowchart of the method according to the teachingsdisclosed herein;

FIG. 7 shows two temporal diagrams illustrating an aspect of stochasticgeneration of stimulation signals according to an aspect of theteachings disclosed herein;

FIG. 8 shows three exemplary probability density distributionsillustrating stochastic electrode selection according to an aspect ofthe teachings disclosed herein;

FIG. 9 shows a schematic flowchart of an aspect according to theteachings disclosed herein; and

FIG. 10 shows a schematic block diagram of components of a neuralauditory prosthesis according to an aspect of the teachings disclosedherein.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 shows a schematic block diagram of a neural auditory prosthesis100 in which some of the main components are illustrated. Generally, theneural auditory prosthesis 100 receives a sound signal, processes thesound signal, and generates an electrical stimulation signal. Dependingon the anatomical site where the electrical stimulation signalstimulates a recipient's nervous tissue, the neural auditory prosthesis100 may comprise a cochlear implant (CI), a brainstem implant (BI), oranother type of implant. In the case of a cochlear implant a stimulationdevice 114 is implanted in the cochlea of the recipient. In the case ofa brainstem implant, the stimulation device 114 may comprise electrodesthat are implanted near the surface of the cochlear nucleus of thebrainstem.

The auditory neural prosthesis 100 typically receives an audio signal atan audio signal interface 102, which generates sound data correspondingto the audio signal. The audio signal interface 102 may comprise amicrophone, an amplifier, and an analog-to-digital converter. The sounddata is transmitted to a filter bank 104 which may be based on asimulation model of the basilar membrane (BM) and/or a simulation modelof the inner hair cells (IHC). The filter bank 104 analyzes the sounddata with respect to frequency contents of the sound data falling into aplurality of frequency ranges. The filter bank 104 may be based on acomputer simulation of a basilar membrane model, or it could be based one.g. a Fast Fourier transform. The filter bank 104 has M output bands,each of the output bands containing a frequency bin signal of aplurality of frequency bin signals 105. In an exemplary implementationof the filter bank 104 a frequency resolution is set to 0.25 Bark/band,which results in 101 bands over the whole audible range. A sampling rateis set to 44100/s in this exemplary implementation. Next, only the M of101 bands are kept, which have characteristic frequencies (CF) closestto the corresponding electrode channels' characteristic frequencies.Typically, the characteristic frequencies of the electrode channels areapproximately logarithmically spaced over the frequency and span over atypical cochlear implant frequency range, i.e. from approximately 250 Hzto 7500 Hz. In another possible implementation different from thementioned exemplary implementation, the filter bank 104 could providethe desired number of M bands directly that are matched to thecharacteristic frequencies of the electrode channel of a cochlearimplant or a brainstem implant. Hence, no selection of frequency bandsis needed anymore in order to reduce the number of frequency bands frome.g. 101 to M, and no superfluous filtering of unused frequency bandsneeds to be carried out.

Besides a computer simulation of a basilar membrane model, the filterbank 104 may further comprise a computer simulation of an inner haircell model, which acts like a rectifier with non-linear properties.

In this exemplary implementation, the complete filter bank 104 providesa set of output data (one sample per band) for each stimulation cycle.If the total stimulation rate (TSR) is not equal to the sampling rate,then the filter bank output may be resampled to the total stimulationrate. The set of output data forms the plurality of frequency binsignals 105.

An amplitude equalizer 106 (AE) is adapted to equalize the plurality offrequency bin signal 105 corresponding to the filter bank bands in a waythat the plurality of frequency bin signals 105 has the same range ofmagnitude among all bands. For example, the amplitude equalizer 106 mayadjust the plurality of frequency bin signals 105 so that the outputrange of each band is in [0,1], where 0.0 and 1.0 are extremescorresponding to a pure tone input with the center frequency of thegiven band at 25.0 and 65.0 dB SPL model perception level, respectively.Optionally, the amplitude equalizer 106 may have an input and a memoryregarding which electrode of the stimulation device 114 was selected forstimulation in the last stimulation cycle, or in one of the laststimulation cycles. If an electrode L was selected for stimulation inthe last stimulation cycle, then the frequency bin signal of thefrequency band that corresponds to the electrode L stimulated in (oneof) the last cycle(s), will be attenuated for the current stimulationcycle by a certain amount, e.g. 10.0 dB. It is believed that theattenuation increases safety by decreasing the risk of over stimulationby repetition, and/or supports the perception of onsets, which may leadto better speech perception.

The equalized frequency bin signals are forwarded to a loudness mappingfunction 108 which maps the amplitude of the samples to an electricalunit representing the magnitude of the stimulation. The low and highlimit of the stimulation magnitude per electrode is individual amongCI-patients. Assuming the patient's limits are C_(low) and C_(high), theloudness mapping function 108 will map the input range of [0, 1] to[C_(low), C_(high)]. The loudness mapping provided by the loudnessmapping function 108 can be non-linear, but has to be monotonic. In anexemplary embodiment which has been implemented for test purposes, theloudness mapping is linear.

A feature extractor and selector module 110 (FES) extracts and/orselects a certain number of the M input samples. Each of the M inputsamples corresponds to an instantaneous value of one of the plurality ofamplitude equalized and loudness-mapped frequency bin signals.Typically, the selected input samples exhibit properties thatdistinguish them from the remaining input samples. The selected inputsamples form a set of selected frequency bin signals 305 (FIG. 3). Theselection of the set of selected frequency bin signals could changequite frequently, such as once every sample period. Therefore, in termsof a duration of the selected frequency signals, the set of selectedfrequency bin signals 305 may be as short as a single sample. Thedetermination of the set of selected frequency bin signals 305 may bebased on various criteria, such as a magnitude of the input samples. Theset of selected frequency bin signals comprises N selected frequency binsignals. These N selected frequency bin signals or samples typicallyhave the largest magnitudes, and they are sorted so that a firstselected frequency bin signal M(1) represents the highest amplitude andM(N) the n-th highest amplitude sample, i.e. the frequency bin signalwith the lowest magnitude of the selected frequency bin signals. In theexemplary implementation mentioned above, the number N of selectedfrequency bin signals in the set of selected frequency bin signals isN=3. Typically, the determination of the set of selected frequency binsignals will be based on the magnitude of the input samples to thefeature extractor and selector module 110. In this case, the set ofselected frequency bin signals may be called a set of strong frequencybin signals, or a set of dominant frequency bin signals. The strongfrequency bin signals or the dominant frequency bin signals are thosesignals that are likely to contain useful information contained in theoriginal sound data that will help the recipient to e.g. understand aword or hear a certain sound. Note that the expression “strong frequencybin signals” does not necessarily mean “strongest frequency binsignals”, although it is contemplated that the set of strong frequencybin signals typically contains the frequency bin signals with the Nlargest magnitudes.

The feature extractor and selector module 110 then selects one frequencybin signal from the set of selected frequency bin signals (or one samplefrom the set of selected samples). This selection is typically random sothat either one of the set of selected frequency bin signals may beselected. The random selection can be biased by means of one or moreselection probability values that are assigned to one or more selectedfrequency bin signals within the selected set. By assigning specificselection probability values to the selected frequency bin signals orsamples, the selection of an ultimately retained frequency binsignal/sample for subsequently driving a corresponding electrode of thestimulation device 114 can be controlled in a manner that the samplewith the largest magnitude is selected more often than the other samplesin the set of selected samples, but not any time. The selectionprobability values may be chosen as a function of a parameter S whichrepresents the probability of choosing the higher magnitude sample. IfS=1.0, then the highest magnitude sample M(1) will be selected in eachcycle, hence, the stimulation is deterministic. If, for example, S=0.8and N=3, then the probability of selecting M(1), MJ(2) or M(3) is 80%,16% and 4%, respectively. If S<1.0, then the stimulation is stochastic,which may account for a better bio-compatibility. In the above mentionedexemplary implementation, the parameter S was chosen to be S=0.9.

A stimulus builder module 112 (SBM) determines other properties (likepulse type, pulse width, etc.) of the stimulus signal based on theproperties of the used stimulation device and on the patient'spreferences. The stimulus builder module 112 also receives data from thefeature extractor and selector module 110, in particular which one ofthe set of selected frequency bin signals has been selected by means ofthe random selection process. The selection of a certain frequency binsignal/sample determines which electrode of the stimulation device 114will be activated. The feature extractor and selector module 110 mayalso provide data regarding a magnitude of the selected sample to thestimulus builder module 112.

To reduce recurrence in stimulation, it may be useful to stochasticallychange one or more stimulus parameter(s) over time. One possiblewaveform for an electrode stimulation signal may be a biphasic pulsewhich has a short gap (approximately 8 μs) between the two phases. Theduration of the gap may be subjected to random variations that can alsobe observed with persons having normal hearing. Such random variationsreflect natural processes and are believed to improve perceptioncapabilities of the recipient of the neural auditory prosthesis 100. Inthe exemplary implementation mentioned above, the phase gap G israndomly varied among subsequent stimulations in the range of [(1−J)·G,(1+J)·G], where J=0.1. Typically, J will be in the interval [0, 1], butcloser to 0 than to 1 (0≦J<<1).

In summary, the neural auditory prosthesis 100 shown in FIG. 1 receivesa sound signal, analyzes the sound signal with respect to its frequencycontents in a number of frequency bands, normalizes the obtainedfrequency bin signals, selects a reduced number of the frequency binsignals, randomly selects a single one of the frequency bin signals, andgenerates an electrode stimulation signal based on parameters andproperties of the selected frequency bin signal. Finally, the electrodestimulation signal is applied to an electrode corresponding to afrequency band of the selected frequency bin signal.

Some of the above mentioned signal processing blocks or tasks may beomitted. For example, the feature extraction and selection module 110could proceed directly to the random selection of one frequency binsignal without the intermediary step of determining a set of selectedfrequency bin signals. This may be achieved by assigning very smallselection probability values (possibly zero) to those frequency binsignals/samples that have relatively weak magnitudes compared to otherfrequency bin signals/samples.

FIG. 2 shows in diagram A the output of the filter bank 104 over timefor a synthetic input sound consisting of a sine sweep signal (275 Hz to7750 Hz) and of a constant 2000 Hz pure tone with an amplitude of −6 dBFS relative to that of the sweep. The number of frequency bands M is 22,i.e. already reduced to match the number electrodes of the stimulationdevice 114. The diagram of FIG. 2A illustrates a magnitude of afrequency bin signal within a certain frequency band at a certaininstant in time as different shades of gray. The total sample rate TSRis TSR=9000/s. It can be seen that the 2000 Hz pure tone shows up onthree different frequency bands. Furthermore, a low frequency beat canbe observed where the sine sweep signal approaches a frequency of 2000Hz of the constant 2000 Hz pure tone.

The diagram B in FIG. 2 shows an output of the feature of the featureextractor selector module 110 generated on the basis of the filter bankoutput shown in diagram A of FIG. 2. The feature extractor and selectormodule output of diagram B was obtained by configuring the signalprocessing with the following parameters: The repetition penalty P waschosen to be 0.0 dB, i.e. a current frequency bin signal or sample wasnot attenuated if it was used for generating the electrode stimulationsignal in the previous cycle. The parameter S controlling thedistribution of selection probability values among the plurality offrequency bin signals 105 was set to S=1.0. This means that theselection of the frequency bin signal to be used for the generation ofthe actual stimulation signal is deterministic. Hence, the featureextractor and selector module 110 selects the frequency bin signalhaving the highest magnitude.

The diagrams C and D in FIG. 2 show further outputs of the frequencyextractor and selector module 110 obtained under different parametersettings. The filter extractor and selector module output shown indiagram C was obtained by setting the repetition penalty attenuation toP=10.0 dB and the parameter S controlling the distribution of theselection probability values to S=1.0 (deterministic). In the case ofdiagram D, the repetition penalty attenuation was set to P=10.0 dB andthe parameter S was set to S=0.5 (stochastic). Especially in the case ofdiagram D it can be seen that the chosen parameter settings allow thesofter pure tone to show up steadily in the stimulation pattern. Incontrast, the softer pure tone vanished completely in the second half ofthe sine sweep signal for the parameter settings corresponding to thefeature extractor and selector module output shown in diagram B. Theparameter settings valid for diagram C (repetition penalty attenuationand deterministic frequency bin signal selection) shows someimprovement. Introducing a certain degree of randomness, as has beendone for the generation of the feature extractor and selector moduleoutput shown in diagram D, yields a stimulation pattern that reflects amajor part of the relevant information contained in the original inputsound. The diagrams A to D of FIG. 2 cover a time span of 100 ms.

The presented method of electrode stimulation allows for keeping a largeportion of fine temporal structure of an original signal when applied tothe output of the filter bank 104. Among other possible effects,cochlear pressure waves, also called delay trajectories, remain part ofthe stimulation pattern, which is believed to increase speech perceptionin patients. For the same reason, phase-locking, compression andadaptation effects of the (simulated) basilar membrane and inner haircell complex can be relatively faithfully represented, which leads tobetter pitch and onset perception.

The presented system does not inherently depend on block-by-blockprocessing, neither on the audio input side, nor on the stimulationoutput side. An effective lag between two identical devices in abinaural setup using this system would be 1/TSR seconds at maximum, ifthe processing blocks are capable of processing data belonging to onesample period in a substantially immediate manner. Some filter banktechnologies currently in use, such as fast Fourier transformation (FFT)may relay on several sample periods for analyzing an input signal withrespect to the input signal's frequency content, which introduces adelay to the signal processing. In this case, the effective lag betweentwo identical devices in a binaural setup is somewhat random and couldbe as long as the introduced delay, i.e. several sampling periods oreven tens of sampling periods. With the immediate signal processing madepossible by the teachings disclosed herein, horizontal-planelocalization of sound sources is possible to a degree not achieved bymost currently available systems.

Horizontal-plane localization ability with binaural systems may furtherbe improved by stochastically varying the phase gap in the stimulusbuilder module 112 as will be explained in connection with thedescription of FIG. 7.

The inclusion of stochastic processing in the signal processing withinthe feature extractor and selector module 110 and/or the stimulusbuilder module 112 are expected to increase bio-compatibility andoverall perception quality.

FIG. 3 shows a schematic block diagram of the feature extractor andselector module 110 and the stimulus builder module 112. The featureextractor and selector 110 receives a plurality of frequency bin signals105 from the loudness mapping function 108. It is however possible thatthe plurality of frequency bin signals 105 is provided by the filterbank 104 or the amplitude equalizer 106, i.e. the neural auditoryprosthesis 100 does not comprise a loudness mapping function 108. Theplurality of frequency bin signals 105 arrive at a plurality of signalinputs 302. Within the feature extractor and selector module 110 theplurality of frequency bin signals are forwarded to a sorter 304 that isadapted to determine a ranking of the plurality of frequency bin signals105 with respect to a certain criterion, such as a magnitude. Thefrequency bin signals 105 are processed in a piecewise manner, that isthe sorter 304 analyzes pieces of the frequency bin signals that fallwithin a certain time span, for example one sample period.

Information about an order of the plurality of frequency bin signalsdetermined by the sorter 304, or the frequency bin signals arranged inthe determined order themselves, or a part of the plurality of frequencybin signals such as those having the largest magnitudes, is output bythe sorter 304 and provided to a random selector 308. The randomselector 308 selects one frequency bin signal from the ordered set offrequency bin signals, for example, according to a random process whichis controlled by one or more selection probability values. Typically, arelatively high selection probability value is assigned to the frequencybin signal having the largest magnitude. A smaller selection probabilityvalue will be assigned to the frequency bin signal having the secondlargest magnitude, and so on. In FIG. 3 it is assumed that the sorter304 provides a set of frequency bin signals, or of references to thefrequency bin signals, which has N elements. As such, the sorter 304 maycomprise a selector adapted to select a reduced set of selectedfrequency bin signals 305 that has fewer than the plurality of frequencybin signals. A size of N=3 for the reduced set of selected frequency binsignals provides the random selector 308 with the three frequency binsignals having the three largest magnitudes from which the randomselector 308 is adapted to pick one by means of a random process takinginto account the selection probability value(s).

The selection probability values are referenced in FIG. 3 with p(1),p(2), and p(N). The selection probability values are provided to therandom selector 308 by a selection probability value assigner 306 thatsets the selection probability values p(1) for the frequency bin signalhaving the largest magnitude, the selection probability value p(2) forthe frequency bin signal having the second largest magnitude, and alsothe selection probability value p(N) for the frequency bin signal havingthe n'th largest magnitude and which typically is the last oneconsidered in the set of selected frequency bin signals. The selectionprobability value assigner 306 may take a parameter S as an input andthe selection probability values are calculated as a function of theparameter S.

An output of the random selection 308 is either an indicator for anelectrode selected by the random selector 308 or a selected frequencybin signal. In the former case, the selected electrode indicator isprovided to a multiplexer 310 as a control signal. The multiplexer 310comprises a plurality of inputs for the plurality of frequency binsignals 105. In FIG. 3 the plurality of frequency bin signals is passedonto the multiplexer 310 from the sorter 304, but this is only one ofseveral possible implementations. For example, the multiplexer 310 couldbe connected directly to the signal inputs 302. The multiplexer 310connects one of its inputs corresponding to the selected electrodeindicator with an output of the multiplexer 310. Thus, the selectedfrequency bin signal is passed onto an amplitude determination module312. An alternative to providing the multiplexer 310 could be to includethe multiplexer capability in the random selector 308. A random selector308 would then receive the selected frequency bin signals and forwardsone frequency bin signal of the set of selected frequency bin signals tothe amplitude determination module.

The amplitude determination module 312 analyzes the selected frequencybin signal with respect to an amplitude thereof. Typically, an amplitudedetermination is already performed by the sorter 304 so that theamplitude determination module 312 may simply access or use thecorresponding amplitude data provided by the sorter 304. The amplitudedetermination module 312 produces a parameter or a parameter set that isused by an electrode stimulation signal generator 314 which is a part ofthe stimulus builder module 112. The electrode stimulation signalgenerator is adapted to create an electrode stimulation signal based onthe parameter(s) provided by the amplitude determination module 312and/or the feature extractor and selector module 110. The generation mayuse a template for the electrode stimulation signal which is adjustedaccording to the provided parameter(s). The generated electrodestimulation signal is provided to an electrode stimulation signal output316 of the stimulus builder module 112 from where it is passed on to thestimulation device 114. The electrode stimulation signal generator 314also receives the selected electrode indicator from the random selector308 so that the generated electrode stimulation signal may comprise aninformation about the selected electrode to which the electrodestimulation signal shall be applied. Although only a single electrodestimulation signal output 316 is shown in FIG. 3, the electrodestimulation signal generator 314 and the stimulus builder module 112could comprise a plurality of electrode stimulation signal outputs, forexample one output per electrode of the stimulation device 114.

The proposed feature extractor and selector module 110 introduces adegree of randomness in the electrode selection which reflects phenomenathat can be observed in the auditory sense of persons who are nothearing impaired. Since this is a natural phenomenon, the healthyremainder of the auditory sense of the recipient of the neural auditoryprosthesis 100 possibly reacts better to a slightly random signal thanto a completely deterministic signal.

FIGS. 4A and 4B show a schematic flowchart of a method for generating acontrol signal for a neural auditory prosthesis 100. The method startsby receiving a pulse code modulated (PCM) audio signal that has beensampled at a sample rate SR, e.g. 44.1 KHz. Often, the sampling rate SRof the audio signal is higher than a pulse rate of the electrodestimulation signal at the output of the neural auditory prosthesis 100.Therefore, a number of N_(S) samples of the audio signal can beprocessed during one stimulation cycle. In a neural auditory prosthesis100 in which the method or the device according to the teachingsdisclosed herein is implemented, the PCM audio signal is typicallyprovided by components of the neural auditory prosthesis 100 notdepicted in FIG. 3, such as a microphone, an amplifier, and ananalog-to-digital converter.

A first action of the method illustrated in FIG. 4A is to perform outerand middle ear (OME) filtering, as shown in block 402. A basilarmembrane (BM) response calculation is also performed at 402. The basilarmembrane response is a plurality of frequency-filtered signals obtainedby processing the PCM audio signal by means of a simulation model of thebasilar membrane. In a simplified manner, the simulation model of thebasilar membrane can be regarded as a filter bank comprising a pluralityof bandpass filters that are closely spaced in the frequency domain.Block 404 represents the basilar membrane response which comprises 101frequency bin signals having N_(S) samples each. The number of 101frequency bin signals is purely exemplary.

At 406 some of the frequency bin signals from the basilar membraneresponse 404 are chosen for further calculation: This action orfunctional block 406 is called channel chooser (ChCh). In the exemplaryimplementation shown in FIG. 4A normally 22 frequency bin signals out ofthe original 101 frequency bin signals are kept, as can be seen at block408 representing the basilar membrane response after channel choosing(“BM response (ChCh)”). The basilar membrane response after channelchoosing 408 thus comprises 22 frequency bin signals with a length ofN_(S) samples each in the exemplary implementation of FIG. 4A.

The choice of the channels at 406 is typically made by comparingcharacteristic frequencies (CF) of the channels (e.g. a center frequencyof the corresponding frequency bin) with characteristic frequencies ofthe electrodes in the stimulation device 114. For example, the channelsmay be selected so that their center frequencies are closest to thecenter frequencies used in an advanced combination encoder (ACE)strategy of a given recipient of the neural auditory prosthesis 100.

At 410 the data on the chosen channels will first be processed bysimulated inner hair cells tuned to act as high-spontaneous rate (HSR)inner hair cells. The high-spontaneous rate inner hair cells start tooperate at hearing threshold level and saturate at about 65 dB SPL. Thissimulation stage is followed by a synaptic cleft model so that theoutput of the auditory model can be thought of as a neurotransmitterconcentration in the synaptic cleft (denoted as CC data in FIG. 4A atreference sign 412) at given positions along the basilar membrane. A CCdata 412 corresponds to the output of the filter bank 104 and thus theplurality of frequency bin signals 105. The filter bank output isinterfaced to a core strategy module.

As a first action of the interfacing, the filter bank output 412 isresampled in time, at 414 to match the total stimulation rate (alsodenoted as total pulse rate: TPR). This results in a data set CC^(RES)data 416 comprising the 22 resampled frequency bin signals having N_(P)samples each. The value N_(P) may be equal to 1 so that each frequencybin signal in the data set 416 comprises a single (instantaneous) sampleonly. At 418, data elements of the resampled filter bank output 416related to channels not marked as inactive are converted to dB FS units.This is possible, since the CC^(RES) values are non-negative (zeroelements and values related to inactive channels will be translated to−99.9 dB FS values to avoid log domain error). The channel gaincorrection can also be applied, if needed, via simple addition to changethe perceived loudness per channel.

In an exemplary implementation of the method of generating a controlsignal for a neural auditory prosthesis 100 all further processing stepsmay be reside in a loop on a sample-by-sample basis (or on astimulation-cycle basis), so that an operation of a current cycle mayuse the results from a previous cycle.

Starting with the data set 420 of frequency bin signals converted to dBFS values, a repetition penalty 422 is applied to that channel of thedata set 420, which was involved in a stimulation of the correspondingelectrode in the last cycle, or in at least one of the last cycles. Byincreasing the value of the repetition penalty, the probability of arepeated selection of the same electrode in consecutive cycles can bedecreased or even completely disallowed. Applying the repetition penaltyat 422 produces a data set 424 (CC^(PEN) data).

The method continues in FIG. 4B as indicated by the connector A. At 426a loudness mapping is performed. In the exemplary implementationillustrated in FIGS. 4A and 4B the values in each channel of the dataset 424 are analyzed and mapped from a loudness range to a normalizedrange. A lower limit of the loudness range is provided by a thresholdlevel and a higher limit of the loudness range is given by a comfortlevel. Typically, the threshold levels and the comfort levels aredifferent for the chosen channels. The threshold level is mapped to thevalue 0.0 of the normalized range and the comfort level is mapped to thevalue 1.0 of the normalized range. Values between the threshold leveland the comfort level are mapped to values within the normalized range[0.0, 1.0]. The mapping may be linear or non-linear, e.g. according to asquare law, an exponential law, a logarithmic law, or a sigmoid law.Values smaller than the threshold level are mapped to 0.0, while valueslarger than the comfort level are mapped to 1.0. The data set containingthe loudness mapped frequency bin signals is designated by the referencesign 428 in FIG. 4B.

Next, a loudness growth function (LGF) is applied to the loudness mappeddata 428 (CC^(LM) data), at block 430. The loudness growth function mapsthe normalized range [0, 1] to another normalized range [0, 1] by meansof a curve that is individual for each channel of the neural auditoryprosthesis 100. The curves of the plurality of loudness growth functionsfor the plurality of chosen frequency bin signals is controlled by acurve shaping factor which is allowed to vary among the channels. Whilein theory it would be possible to combine the loudness mapping 426 andthe loudness growth function 430, their separation may be easier tohandle for an audiologist when adjusting the neural auditory prosthesis100 to a specific recipient. A block 432 represents the data of thechosen channels after the loudness growth function (CC^(LGF) data).

In the next step, the three channels having the largest amplitude valuesin the data set 432 are searched for. The determined maximum values aresorted in descending order and are stored along with their original(channel) indices in a data structure CC^(MAX). A first data elementCC^(MAX)[0] of the data structure CC^(MAX) 436 represents the largestmaximum, CC^(MAX)[1] the second largest maximum and CC^(MAX)[2] thethird largest maximum. The number of three maximum values is exemplary.For the purposes of the method disclosed herein, any number ofdetermined maximum values equal to or larger than two may be used. Itcan happen that all chosen channels have signal values in the data set432 that are below a processing threshold. In this case, a nullstimulation will be scheduled for the current cycle and all consecutiveprocessing steps are skipped.

If not only the largest maximum CC^(MAX)[0] was found, but also thesecond largest maximum CC^(MAX)[1] and possibly further largest maximumsaccording to the order determined by the processing block 434, then thenext task is to select one of them. Based on the settings controlling arandomness of the selection process, such as the parameter S (FIG. 3),this selection can be deterministic or stochastic. The parameter Srepresents the probability of choosing the largest maximum. If S=1.0,then in each cycle the largest maximum CC^(MAX)[0] will be selected andthe stimulation is deterministic. If S<1.0, then the stimulation isstochastic, which may account for better bio-compatibility. Theapplication of the parameter S may be recursive, that is in a firstrecursion the selection probability values p(1) for the largest maximumis determined and by calculating 1−p(1) the combined probability for thesecond largest maximum to the n'th largest maximum is calculated. In asubsequent recursion, the selection probability value for the secondlargest maximum p(2) is determined by calculating (1−S). If, forexample, three peaks were found and S=0.8, then the selectionprobability values p(1), p(2), and p(3) of selecting CC^(MAX)[1],CC^(MAX)[2] or CC^(MAX)[3] is 80%, 16% and 4%, respectively. The actionof choosing one of three peaks is represented by block 438 in FIG. 4B.Block 440 represents the selected data element CC^(SEL) containing1×N_(P) samples.

At block 424 a volume setting is performed on the selected frequency binsignal or sample. The volume setting comprises adjusting a dynamic rangedetermined by the difference of the threshold and comfort levels of theelectrical stimulation. If no custom volume is specified, then a defaultsetting for the volume is used. In the same processing step thevolume-adjusted values are mapped to a current level range of [thresholdcurrent level, comfort current level] and rounded to “current level”integers. Furthermore, stimulation parameters are gathered to form astimulus parameter set (denoted as StimPar data in FIG. 4B, referencesign 444). The stimulation parameters may be a width of the electrodestimulation signal (e.g., a duration of a gap occurring during theelectrode stimulation signal) and an indicator of the electrode to whichthe electrode stimulation signal shall be applied. The indicator of theelectrode to be used for stimulation can typically be taken from thedata set 440.

At the optional block 446 a possibility is given for stochasticallyvarying different ones of the parameters controlling the generation ofthe electrode stimulation signal. As an example, the possibility may begiven to vary the phase gap length property of a biphasic electrodestimulation signal between well-defined limits in a stochastic manner.This random variation adds some irregularity to the stimulation signal,which reduces periodic characteristics, while preserving fine temporalstructures of the original signal. For example, a phase gap variation ΔGmay be introduced which causes a length of the phase gap to be variedamong subsequent stimulation cycles in a range of [(1−ΔG)·G₀,(1+ΔG)·G₀]. The variable G₀ represents an average length of the phasegap. The phase gap variation ΔG is in the range [0, 1] and typically hasa value much smaller than 1, e.g. ΔG=0.1. The application of such aphase gap variation may be subjected to a compatibility with a currentstimulation mode (determined e.g. by the stimulation rate). Inparticular, it may be that the current stimulation mode allows certainmaximal values for the phase gap variation. In case the application of aphase gap variation or of the value of the phase gap variation is notcompatible with the current stimulation mode the action 446 may becompletely skipped.

Whether or not a stochastic variation of one or more stimulus parametershas been performed, a corresponding parameter set 448 is provided. Thestimulation parameter set 448 is then used to generate and apply acorresponding electrode stimulation signal at block 450.

Exemplary configuration parameters related to the auditory filter bank104 are listed in the following table:

Configuration Key Exemplary values Comment SFREQ 44100.0 Samplingfrequency (in Hz). BM_NO_SECTIONS 101 Number of BM-sections to besimulated. BM_MAX_LAT_COUPL 8 Number of laterally coupled sections.BM_MAX_BARK 25 Max frequency that the BM should simulate (in Bark).BM_DELTA_Z 0.25 Frequency spacing of adjacent BM-sections (in Bark).BM_BM_PREAMP −50.0 BM input and output range amplification (in dB). TheyBM_FACTOR −113.0 account for the right working range of the BM.BM_NO_OME 0 Whether to disable outer and middle ear filtering.BM_HWM_CQNU_FACTOR 0.6 Factors to fine-tune the delay trajectory shapes.BM_HWM_C_FACTOR 3.0 BM_USAT_FACTOR1 5.0 Factors to fine-tune thenon-linear amplitude BM_USAT_FACTOR2 200.0 characteristics of the BMmodel. BM_REAL_CFS 84.9, 97.0, . . . , 19077.7 Estimated channelfrequencies at 55.0 dB SPL. POST_BM_CHANNELS 22 Number of sections thechannel chooser should keep. BM_CHANNEL_MAP 11, 16, . . . , 86 Sectionsto keep by channel chooser. C_CILIA −55.0 Coupling factor: IHC releaseprobability to cleft (in dB). KT_FACTOR 5.69 Cleft concentrationamplification factor.

Exemplary parameters relative to the operation of the amplitudeequalizer 106, the loudness mapping function 108, the feature extractorand selector module 110, and the stimulus builder module 112 are listedin the following table:

Configuration Key Exemplary values Comment MAP_CHANNEL_AC 1, 1, 1, 1, 1,1, 1, . . . , 1 Flag active channels (1: active, 0: inactive).MAP_CHANNEL_TL 0, 0, 0, 0, 0, 0, 0, . . . , 0 Threshold/comfort levelper channel (in Current MAP_CHANNEL_CL 255, 255, 255, . . . , 255Level), as in the personalized MAP of the CI-user. MAP_CHANNEL_GAIN 0.0,0.0, 0.0, . . . , 0.0 Gain correction per channel (in dB).MAP_CHANNEL_TDB −37.6, −38.8, . . . , −67.0 CC^(db) data levels for apure tone of the respective MAP_CHANNEL_CDB −20.4, −22.5, . . . , −33.7center frequency with T-SPL/C-SPL loudness. MAP_CHANNEL_CSF 0.01, 0.01,. . . , 0.01 Curve shaping factors for loudness mapping.MAP_TOT_PULSRATE 9000 Total pulse rate (in 1/s). MAP_PHASE_WIDTH 25.0Phase width (in μs). MAP_PHASE_GAP 8.0 Inter-phase gap (in μs).MAP_REF_ELECTRODE −3 Reference electrode (−3, −2 or −1). MAP_GAP_JITTER0.1 Inter-phase gap jitter (1.0: max, 0.0: none). MAP_MAX_RANDOMNESS 0.1Level of randomness in maxima selection (0.5: max). MAP_REP_PENALTY 10.0Level with which a filter-bank band should be attenu- ated if alreadystimulated in the last cycle (in dB). MAP_DEFAULT_VOLUME 1.0 Defaultvolume in stimulation (1.0: max, 0.0: min).

FIG. 5 shows a response of a basilar membrane and of a model thereof fora 1000 Hz pure tone at various loudness levels. The observationsobtained from the original basilar membrane are represented as blacksquares in FIG. 5 and show that the basilar membrane has a relativelyhigh sensitivity for loudness differences in a low loudness range, aswell as in a high loudness range. In an intermediate loudness rangebetween approximately 40 dB SPL and 80 dB SPL the curve is relativelyflat, indicating that the basilar membrane has a relatively lowsensitivity towards loudness in this range. A simple linear model of thebasilar membrane behavior is shown in FIG. 5 as a thick full line thatasymptotically approaches the observation at high loudness levels. Thelinear model substantially neglects a basilar membrane response forloudness levels below approximately 60 dB SPL. A model characteristicsrepresented by a thin full line in FIG. 5 follows the observations moreclosely, while the active characteristics drawn as a thick dashed lineare still closer to the observations. A basilar membrane input andoutput range amplification and the factors for fine-tuning the amplitudecharacteristics may be tuned in a way that the non-linear characteristicof the simulated basilar membrane best fit experimental data.

FIG. 6 shows an exemplary, schematic flowchart of a method according toone aspect of the teachings disclosed herein. After a start of themethod at 601 a plurality of frequency bin signals is received at 602.The plurality of frequency bin signals may correspond to the electrodesavailable in a stimulation device 114 of an auditory prosthesis 100. Atan optional action 603 a set of strong frequency bin signals isdetermined. A frequency bin signal may qualify as “strong” if itfulfills one or more criteria, such as having a large magnitude.

At another optional action 604 the set of strong frequency bin signalsis sorted according to magnitude so that a frequency bin signal havingthe largest magnitude, a frequency bin signal having the second largestmagnitude, and so on, can be determined.

At least one selection probability value is assigned to at least one ofthe strong frequency bin signals at 605. Typically, the selectionprobability value is assigned to all of the strong frequency bin signalsin the selected set of strong frequency bin signals. At an action 606one of the strong frequency bin signals is selected by means of a randomprocess that is “biased” by the selection probability value(s) assignedto the strong frequency bin signals at the previous action 605.Typically, the frequency bin signal having the largest magnitude will beselected with a higher probability than the frequency bin signal withthe second largest magnitude, and so on. It is, however, possible thatthe second largest frequency bin signal or even lower ranking frequencybin signals, in terms of their magnitude, are selected during the action606 if their assigned selection probability value is non-zero.

The electrode stimulation signal is then generated at 607 for anelectrode that corresponds to an index of the selected strong frequencybin signal. The method ends at 608. The method is typically repeatedonce per stimulation cycle.

FIG. 7 illustrates another option for introducing a degree of randomnessto the generation of the electrode stimulation signal. The upper diagramin FIG. 7 shows a wave form of two consecutive biphasic stimulationpulses. Each of the two biphasic stimulation pulses begins with anegative pulse followed by a gap G. After the gap G a positive pulsefollows. A duration of the gap G is given by G₀+ΔG(t), where G₀ is anaverage duration of the gap G and the term ΔG(t) is a time-varying,random portion of the gap duration. Thus, the two consecutive biphasicpulses of the electrode stimulation signal may have different gapdurations.

The lower diagram of FIG. 7 shows a waveform illustrating the temporalevolution of the time-variable, random portion ΔG(t), measured inmicroseconds. A new value for ΔG(t) is determined in a periodical,random manner. For the sake of illustration, several randomdeterminations are shown in the lower diagram of FIG. 7, even though onerandom determination per stimulation cycle may be sufficient. Thetime-variable, random portion ΔG(t) of the gap duration may assume anyvalue between two limits −ΔG_(max) and +ΔG_(min). The probabilitydensity distribution may be suitably chosen, such as a uniformdistribution or a Gaussian distribution.

The randomly determined duration of the phase gap may be used in block446 of FIG. 4B.

In FIG. 8 three different probability distribution densities are shownfor the random process of selecting one frequency bin signal, andconsequently, a corresponding electrode via which the electrodestimulation signal of the current stimulation cycle will be applied. Inthe upper diagram of FIG. 8, the parameter S has been chosen to be S=1.This means that the probability of selecting the largest magnitudefrequency bin signal p(1) is equal to 1, e.g. 100%. The selectionprobability values for the remaining frequency bin signals in the set ofstrong frequency bin signals is p(2)=p(3)=0. This means that therandomness in the frequency bin signal selection process has beeneliminated and that the frequency bin signal selection is in factdeterministic.

In the middle diagram of FIG. 8 the parameter S has the value 0.9. Thisleads to the following selection probability values: p(1)=0.9;p(2)=0.09; and p(3)=0.01. In the lower diagram the parameter S is equalto 0.8. The resulting selection probability values are p(1)=0.8;p(2)=0.16; and p(3)=0.04.

FIG. 9 shows a schematic flow diagram of a method according to oneaspect of introducing a degree of randomness to the generation of anelectrode stimulation signal. After the start at 901 a plurality offrequency bin signals is received at 902. At a block 903 a frequency binsignal from the plurality of frequency bin signals is selected. Notethat in the context of this aspect of the teachings disclosed herein,none of the frequency bin signals could be selected or several ones ofthe frequency bin signals could be selected.

At a block 904 the stimulation signal generation parameters used forgenerating the ultimate electrode stimulation signal is varied at randomwithin predefined bounds. An electrode stimulation signal forapplication to a corresponding electrode is then generated at 905according to the stimulation signal generation parameters determined andvaried at the action 904. The generated electrode stimulation signal(s)is (are) then applied to the electrode(s) corresponding to the selectedfrequency bin signal(s). At the block 906 the method ends.

FIG. 10 shows a schematic block diagram according to an aspect of theteachings disclosed herein. The plurality of frequency bin signals 105is received at a plurality of signal inputs 302 from where they aredistributed to a multiplexer 310 and an evaluator 1006 which is part ofa feature extractor and selector module 1010. The evaluator 1006 may forexample implement a selection method for the selective frequency binsignals illustrated and described in the context of FIG. 3. In thealternative, the evaluator 1006 could be implemented according to adeterministic selection scheme. In a manner similar to the oneillustrated at FIG. 3, the selected frequency bin signal is forwarded tothe amplitude determination 312 where its amplitude is determined.

The stimulus builder module 1012 comprises the electrode stimulationsignal generator 314 which receives the determined amplitude value andalso an indicator for the selected electrode/frequency bin signal. Thestimulus builder module 1012 further comprises a parameter modifier 1008connected to the electrode stimulation signal generator 314. Theparameter modifier 1008 is adapted to provide modified values ofstimulation signal generation parameters to the electrode stimulationsignal generator 314. The parameter modifier 1008 may comprises arandomizer so that random values of the stimulation signal generationparameters are produced by the parameter modifier 1008. In thealternative, the parameter modifier 1008 may modify the stimulationsignal generation parameters in a predetermined manner, thus simulatinga random behavior. The electrode stimulation signal generator 314generates corresponding electrode stimulation signals which areavailable at an electrode stimulation signal output 316. An example of astimulation signal generation parameter subject to random variations(within certain bounds) is the duration of the phase gap of a biphasicpulse.

The electrode stimulation signal generator 314 may use predefinedtemplates for the electrode stimulation signals. These templatestypically offer a number of options to modify a resulting electrodestimulation signal by adjusting one or more stimulation signalgeneration parameters.

Although some aspects have been described in the context of anapparatus, it is clear that these aspects also represent a descriptionof the corresponding method, where a block or device corresponds to amethod step or a feature of a method step. Analogously, aspectsdescribed in the context of a method step also represent a descriptionof a corresponding block or item or feature of a correspondingapparatus. Some or all of the method steps may be executed by (or using)a hardware apparatus, like for example, a microprocessor, a programmablecomputer or an electronic circuit. In some embodiments, some one or moreof the most important method steps may be executed by such an apparatus.

Depending on certain implementation requirements, embodiments of theinvention can be implemented in hardware or in software. Theimplementation can be performed using a digital storage medium, forexample a floppy disk, a DVD, a Blue-Ray, a CD, a ROM, a PROM, an EPROM,an EEPROM or a FLASH memory, having electronically readable controlsignals stored thereon, which cooperate (or are capable of cooperating)with a programmable computer system such that the respective method isperformed. Therefore, the digital storage medium may be computerreadable.

Some embodiments according to the invention comprise a data carrierhaving electronically readable control signals, which are capable ofcooperating with a programmable computer system, such that one of themethods described herein is performed.

Generally, embodiments of the present invention can be implemented as acomputer program product with a program code, the program code beingoperative for performing one of the methods when the computer programproduct runs on a computer. The program code may for example be storedon a machine readable carrier.

Other embodiments comprise the computer program for performing one ofthe methods described herein, stored on a machine readable carrier.

In other words, an embodiment of the inventive method is, therefore, acomputer program having a program code for performing one of the methodsdescribed herein, when the computer program runs on a computer.

A further embodiment of the inventive methods is, therefore, a datacarrier (or a digital storage medium, or a computer-readable medium)comprising, recorded thereon, the computer program for performing one ofthe methods described herein. The data carrier, the digital storagemedium or the recorded medium are typically tangible and/ornon-transitionary.

A further embodiment of the inventive method is, therefore, a datastream or a sequence of signals representing the computer program forperforming one of the methods described herein. The data stream or thesequence of signals may for example be configured to be transferred viaa data communication connection, for example via the Internet.

A further embodiment comprises a processing means, for example acomputer, or a programmable logic device, configured to or adapted toperform one of the methods described herein.

A further embodiment comprises a computer having installed thereon thecomputer program for performing one of the methods described herein.

A further embodiment according to the invention comprises an apparatusor a system configured to transfer (for example, electronically oroptically) a computer program for performing one of the methodsdescribed herein to a receiver. The receiver may, for example, be acomputer, a mobile device, a memory device or the like. The apparatus orsystem may, for example, comprise a file server for transferring thecomputer program to the receiver.

In some embodiments, a programmable logic device (for example a fieldprogrammable gate array) may be used to perform some or all of thefunctionalities of the methods described herein. In some embodiments, afield programmable gate array may cooperate with a microprocessor inorder to perform one of the methods described herein. Generally, themethods are advantageously performed by any hardware apparatus.

The above described embodiments are merely illustrative for theprinciples of the present invention. It is understood that modificationsand variations of the arrangements and the details described herein willbe apparent to others skilled in the art. It is the intent, therefore,to be limited only by the scope of the impending patent claims and notby the specific details presented by way of description and explanationof the embodiments herein.

While this invention has been described in terms of several embodiments,there are alterations, permutations, and equivalents which fall withinthe scope of this invention. It should also be noted that there are manyalternative ways of implementing the methods and compositions of thepresent invention. It is therefore intended that the following appendedclaims be interpreted as including all such alterations, permutationsand equivalents as fall within the true spirit and scope of the presentinvention.

The invention claimed is:
 1. A method of generating a control signal fora neural auditory prosthesis, based on an audio signal, the methodcomprising: receiving a plurality of frequency bin signals; determiningwhether one electrode of the neural auditory prosthesis had beenselected for stimulation during a previous stimulation cycle;attenuating a corresponding frequency bin signal of the plurality offrequency bin signals that corresponds to the determined electrodestimulated during the previous stimulation cycle, wherein thecorresponding frequency bin signal is attenuated for a currentstimulation cycle by a predetermined amount; selecting a reduced set offrequency bin signals from the plurality of frequency bin signals,depending on magnitudes of the plurality of frequency bin signals, thereduced set of frequency bin signals having fewer frequency bin signalsthan the plurality of frequency bin signals; assigning a selectionprobability value to each frequency bin signal of the reduced set offrequency bin signals, depending on each frequency bin signal of thereduced set of frequency bin signals; selecting one frequency bin signalof the reduced set of frequency bin signals by a random process takinginto account the selection probability value assigned to the onefrequency bin signal of the reduced set of frequency bin signals; andgenerating an electrode stimulation signal for application to anelectrode of the neural auditory prosthesis corresponding to a frequencyof the selected frequency bin signal; wherein frequency bin signals ofthe reduced set of frequency bin signals with relatively highermagnitudes are assigned higher-valued selection probability values thanselection probability values assigned to frequency bin signals of thereduced set of frequency bin signals with relatively lower magnitudes.2. The method according to claim 1, further comprising: varying astimulation signal generation parameter used for generating theelectrode stimulation signal.
 3. The method according to claim 2,wherein a template for creating the electrode stimulation signalcomprises a temporal gap in which the template is substantiallyzero-valued between two non-zero sections, and wherein the stimulationsignal generation parameter subject to random variation is a duration ofthe temporal gap.
 4. The method according to claim 1, furthercomprising: performing an amplitude equalization on the receivedplurality of frequency bin signals.
 5. The method according to claim 1,prior to assigning the selection probability values to the reduced setof frequency bin signals, further comprising: mapping an amplitude ofeach one of the plurality of frequency bin signals to a loudness-mappedrepresentation of the amplitude, the mapping being based onpatient-specific conditions.
 6. The method according to claim 1, whereinthe plurality of frequency bin signals are received from a filter bankbased on a simulation of at least one of a basilar membrane and an innerhair cell.
 7. A non-transitory computer readable digital storage mediumcomprising stored thereon a computer program comprising a program codefor performing, when running on a computer, a method for signalprocessing of a signal in a neural auditory prosthesis to generate acontrol signal for the neural auditory prosthesis, the methodcomprising: receiving a plurality of frequency bin signals; determiningwhether one electrode of the neural auditory prosthesis had beenselected for stimulation during a previous stimulation cycle;attenuating a corresponding frequency bin signal of the plurality offrequency bin signals that corresponds to the determined electrodestimulated during the previous stimulation cycle, wherein thecorresponding frequency bin signal is attenuated for a currentstimulation cycle by a predetermined amount; selecting a reduced set offrequency bin signals from the plurality of frequency bin signals,depending on magnitudes of the plurality of frequency bin signals, thereduced set of frequency bin signals having fewer frequency bin signalsthan the plurality of frequency bin signals; assigning a selectionprobability value to each of frequency bin signal of the reduced set offrequency bin signals, depending on each frequency bin signal of thereduced set of frequency bin signals; selecting one frequency bin signalof the reduced set of frequency bin signals by a random process takinginto account the selection probability value assigned to the onefrequency bin signal of the reduced set of frequency bin signals; andgenerating an electrode stimulation signal for application to anelectrode of the neural auditory prosthesis corresponding to a frequencyof the selected frequency bin signal; wherein frequency bin signals ofthe reduced set of frequency bin signals with relatively highermagnitudes are assigned higher-valued selection probability values thanselection probability values assigned to frequency bin signals of thereduced set of frequency bin signals with relatively lower magnitudes.8. An auditory stimulation signal processing device comprising: aplurality of signal inputs configured to receive a plurality offrequency bin signals; an amplitude equalizer configured to perform anamplitude equalization on the plurality of frequency bin signals,determine whether one electrode of a neural auditory prosthesis had beenselected for stimulation during at least one previous stimulation cycleamong a certain number of previous stimulation cycles, and attenuate acorresponding frequency bin signal that corresponds to the determinedelectrode stimulated during the previous stimulation cycle among thecertain number of the previous stimulation cycles, wherein the amplitudeequalizer is configured to attenuate the corresponding frequency binsignal for a current stimulation cycle by a predetermined amount; asorter configured to select a reduced set of frequency bin signals fromthe plurality of frequency bin signals, depending on magnitudes of theplurality of frequency bin signals, the reduced set of frequency binsignals having fewer frequency bin signals than the plurality offrequency bin signals; a selection probability value assigner configuredto assign a selection probability value to each frequency bin signal ofthe reduced set of frequency bin signals, depending on each frequencybin signal of the reduced set of frequency bin signals; a randomselector configured to select one frequency bin signal from the reducedset of frequency bin signals by a random process taking into account theselection probability value assigned to the one frequency bin signal ofthe reduced set of frequency bin signals; and an electrode stimulationsignal generator configured to generate an electrode stimulation signalfor application to an electrode of the neural auditory prosthesis, theelectrode corresponding to a frequency of the selected frequency binsignal; wherein frequency bin signals of the reduced set of frequencybin signals with relatively higher magnitudes are assigned higher-valuedselection probability values by the selection probability value assignerthan selection probability values assigned to frequency bin signals ofthe reduced set of frequency bin signals with relatively lowermagnitudes.
 9. The auditory stimulation signal processing deviceaccording to claim 8, further comprising a modifier configured to vary astimulation signal generation parameter used for generating theelectrode stimulation signal.
 10. The auditory stimulation signalprocessing device according to claim 8, wherein the plurality of signalinputs are connectable to a filter bank, the filter bank being based ona simulation of at least one of a basilar membrane and an inner haircell.